Jiangsu Provincial Key Lab for Organic Solid Waste Utilization; Ministry of Agriculture, Key Laboratory of Plant Nutrition and Fertilization in Low-Middle Reaches of the Yangtze River; College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.
Sensors (Basel). 2017 Mar 8;17(3):538. doi: 10.3390/s17030538.
Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha. The mid-portion leaves on the year's shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets-both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration ( = 1000) and validation ( = 420) of this model resulted in high R² values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R² = 0.77) and 2014 (R² = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24-27 g/kg.
非破坏性且及时地测定叶片氮(N)浓度对于梨园的 N 管理至关重要。在一个商业梨园进行了为期两年的田间试验,设置了五个施氮量:0(N0)、165(N1)、330(N2)、660(N3)和 990(N4)kg·N·ha。在盛花期后 50 和 80 天(DAB),在田间分别对当年新梢中部叶片进行光谱测量和实验室氮浓度测定。比较了三种田间光谱测量方法(25°裸光纤在太阳条件下、植物探头附有黑色背景、植物探头附有白色背景)。还研究了四种化学计量技术(主成分回归、PCR;偏最小二乘回归、PLSR;逐步多元线性回归、SMLR;和反向传播神经网络、BPNN)和三种植被指数(差光谱指数、归一化差光谱指数和比光谱指数)的建模性能。由于 25°视场方法获得的反射率相关性较低,因此所有建模都是在两个光谱数据集上进行的,这两个数据集都是通过植物探头获得的。结果表明,在 PLSR 建立的模型和用黑色背景测量的光谱中,发现了最佳的建模和预测精度。该模型的校准随机分离子集(n = 1000)和验证子集(n = 420)分别得到了 0.86 和 0.85 的高 R²值,以及低于 6%的平均相对误差。此外,在 2015 年(R² = 0.77)和 2014 年(R² = 0.59)的盛花期 50 DAB 采样中,叶片 N 浓度与果实产量之间的决定系数更高。因此,建议通过可见/近红外光谱在盛花期 50 DAB 测定叶片 N 浓度,阈值应为 24-27 g/kg。